Patients, as the consumer of pharmaceutical products, are the most important contributor to drug safety surveillance. Yet studies show that the rate of patient participation is low when traditional methods of reporting drug effects-such as spontaneous reporting systems-are used. Studies have also documented that patients report their experiences in different ways and in more detail than healthcare professionals. Furthermore, traditional methods of collecting patient reports are typically slow and costly. The objective of the proposed research is to investigate whether patient experiences of drug effects can be detected directly from their posts on Twitter. We propose to retrieve and collect Twitter posts and conversations related to a list of 100 pre-selected drugs, to extract from the collection the posts that demonstrate patients' personal experiences, and to identify any effects mentioned in the drug-related personal experience Tweets. This data will be analyzed to assess the relationships between different drugs and reported beneficial or adverse effects, and to ascertain whether or not the effects have been previously reported. A data processing pipeline will be devised to automate many components of the data collection and analysis process which is expected to reduce the cost and speed up the discovery of any potential drug effects. A machine-learning-based method will be developed to identify the Twitter posts that demonstrate a user's experience. The National Library of Medicine's MetaMap software will be employed to map the word phrases identified in Twitter posts to UMLS semantic types, among which many are related drug effects. To assess the validity of our approach, healthcare professionals with knowledge and experience in drug safety surveillance will participate in verifying and annotating drug-related personal experience Twitter posts, and confirming the drug effect relationship.